AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The Agentic Reward System (ARS) is introduced, an inference framework for progress reward modeling with general-purpose vision-language models (VLMs), without additional reward-model training, and its results suggest that structured inference and verification can improve the usefulness of general-purpose VLMs for robot...
RoboFolDeX is introduced, a physical-world benchmark built entirely from real-robot data, with garment folding as its primary task, and a fair real-robot evaluation platform for externally submitted policies, with standardized tasks, held-out physical objects, controlled initializations, and a unified execution protoco...
Experiments on a real robot platform demonstrate that MaskHarness-WAM substantially outperforms limited-horizon policies on sequential multi-object manipulation, showing its effectiveness in extending local manipulation skills to reliable long-horizon execution.
The Causal Semantic World Action Model (CSWAM) is presented, which augments FastWAM with a causal semantic expert built on V-JEPA 2.1, which provides temporally grounded representations of semantic state changes and motion with less dependence on appearance-specific details.
DSWAM is introduced, a Dual-System World Action Foundation Model for fine-grained robot manipulation and built and evaluated under the DeMaVLA real-world deformable manipulation setting with matched robot platform, pretraining data, post-training data, and evaluation criteria.
MECo-WAM is proposed, a Multi-Expert Co-Training World Action Model that injects action-relevant 4D geometric priors into video-action representations while preserving the original lightweight inference graph.
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